In recent years, use of digital images is advanced in the field of CAD. Therefore, by digitizing a medical image, a possibility of diagnostic form which was difficult in conventional diagnosis using silver salt photograph comes out.
More specifically, in the conventional diagnosis, in a case where plural X-ray photographs which were taken at different points in time during observation of patient's condition are compared for diagnosis, the films on which the X-ray photographs have been respectively developed are generally hung on a light box (schaukasten), and the hung films are actually compared and read.
Meanwhile, in the case where the digital images are used in the diagnosis, the two digital images which were taken at different points in time with respect to one patient are subjected to registration so that the normal anatomical structure on one digital image conforms to that of the other digital image, and then a subtraction process is executed to the two digital images, whereby a difference image is generated and output. Subsequently, the output difference image is compared with the pair of the two original digital images, whereby it is possible to more accurately grasp a change between the two original images.
Such a difference image generation method is disclosed in, for example, Japanese Patent Application Laid-Open No. H07-037074 which corresponds to U.S. Pat. No. 5,359,513 and is called the document 1 hereinafter. That is, according to the generation method disclosed in the document 1, two chest X-ray images respectively taken at different points in time are subjected to registration, and a difference image can be generated. Here, it should be noted that such a subtraction process is called a temporal subtraction process.
Subsequently, the schematic constitution of the device which achieves the temporal subtraction process as disclosed in the document 1 will be explained with reference to FIG. 25.
In FIG. 25, first, a pair of medical digital images input by an image input unit 1 is subjected to a density correction process by a pre-processing unit 11, and is then input to an ROI (region of interest) matching unit 12. In the ROI matching unit 12, a matching process is executed with respect to plural set ROI's (regions of interest) by calculating a cross-correlation coefficient, and a shift vector which indicates a displacement amount in the pair of the medical digital images (two images) is calculated with respect to each ROI.
Then, in a polynomial interpolation unit 13, the calculated shift vector is subjected to approximate interpolation by a two-dimensional n-degree polynomial. Subsequently, in a registration unit 5, non-linear distortion is applied to either one of the two images. Moreover, in a subtraction operation unit 6, subtraction is executed between the pixels at the corresponding locations, whereby a difference signal is generated. After then, in a post-processing unit 7, a post-process including a gradation process and the like is executed to the difference signal, and the processed signal is output to an output unit 8.
Incidentally, a temporal subtraction technique for a chest X-ray image is the technique for dealing with first and second images of a common subject which is a part of a human body taken at different points in time. More specifically, the temporal subtraction technique corrects deformation of a lung field which occurs due to various factors such as forward-and-backward and rightward-and-leftward movements of the subject, breath of the subject, a change of an X-ray tube irradiation angle, and the like, executes a subtraction process, and then extracts the portions including changes as a difference image from the two images. By applying the above temporal subtraction technique, it is possible to extract the image components corresponding to only a change of the seat of a disease from the first and second images as taking no account of the common normal tissues such as bones, blood vessels and the like. Consequently, particularly in a temporal subtraction CAD technique, it is possible to clinically expect early detection of lesion, early detection of the seat of a disease hidden behind the normal constitutions such as rib bones, blood vessels and the like, prevention of oversight of lesion, and rapid interpretation of radiogram.
In any case, the main factor of the temporal subtraction technique is a registration technique for correcting the deformation occurring between the first and second images. By the way, Japanese Patent Application Laid-Open No. 2002-032735 which corresponds to U.S. Publication No. 2001048757 and is called the document 2 hereinafter discloses the conventional temporal subtraction technique. More specifically, in the conventional temporal subtraction technique like this, the process as shown in FIG. 26 is executed. That is, the first image (original image or past image) and the second image (current image) are first read, and then a template ROI is uniformly set within the region of a lung field of the first image. Subsequently, in the second image, a search ROI is set at the location corresponding to the template ROI of the first image. At this time, in the search ROI of the second image, the location corresponding to the center of the template ROI of the first image is searched, and the transition from the center of the template ROI of the first image to the relevant location in the search ROI of the second image is recorded as the shift vector.
In case of actually recording the shift vector, the coordinates of the center of the template ROI and the transition from the center of the template ROI to the relevant location in the search ROI are recorded. Typically, in case of achieving conformation (matching) of the ROI's, a degree of matching is used as the weight of the shift vector. Then, ordinarily, in case of achieving the matching by using cross-correlation of the ROI's a correlation coefficient itself is used as the weight of the shift vector as it is. Besides, in case of achieving the matching by using an SSDA (Sequential Similarity Detection Algorithm), the result acquired by calculating and normalizing an inverse number of a residual error is used as the weight of the shift vector. After then, interpolation with use of a polynomial is executed to the shift vector by using the acquired weight, and the second image is warped to the first image to acquire the difference.
However, in the above document 1, when the shift vector acquired by executing the matching (ROI matching) with respect to each of many ROI's is interpolated by polynomial approximation, the coefficient of the polynomial is determined by a method of least squares or the like. For this reason, there is a technical problem that it takes a long time to execute such a process.
Moreover, in the ROI matching, if plural similar patterns exist in the subject, there is a limit in accuracy of the matching. Consequently, according to circumstances, it is impossible to avoid including a serious error in the shift vector. In such a case, if the shift vector is interpolated by using the method of least squares, the included error influences other shift vectors, whereby displacement or misregistration occurs entirely. For this reason, there is a technical problem that noise components increase in the difference image.
Incidentally, as described below, a chest simple X-ray photograph includes various regions of which the information amounts are different from others.
That is, a clavicle and a body border portion, while the gradation information is poor or simple, the edge information is wealthy. In a lung field edge portion, both the gradation information and the edge information are wealthy. Besides, in a heart and a diaphragm, both the gradation information and the edge information are simple or poor. Thus, for example, in the document 2, when the matching is executed by using the ROI of which the gradation information or the edge information is poor, it cannot necessarily be said that the matching of the ROI is accurately executed even if the weight of its shift vector is high, and it is impossible in this case to judge whether or not the information of the shift vector is correct. For this reason, there is a technical problem that it negatively influences subsequent processes.
In addition, with respect to the shift vector of the ROI of which the texture including the gradation information, the edge information and the like is wealthy, it is desirable to execute the interpolation with accuracy higher than that for the shift vector of the ROI of which the texture is low, whereby more accurate shift vector interpolation is necessary.